Dynamic covariate balancing: estimating treatment effects over time with potential local projections
It addresses causal inference challenges in non-experimental panel data for researchers and practitioners, representing an incremental improvement with specific technical extensions.
This paper tackles the problem of estimating treatment effects in panel data when treatments change dynamically over time, proposing a balancing method that handles high-dimensional covariates and sequential assignments while establishing inferential guarantees even with many observable characteristics.
This paper studies the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes, and treatments; (ii) outcomes and time-varying covariates to depend on the trajectory of all past treatments; (iii) heterogeneity of treatment effects. Our approach recursively projects potential outcomes' expectations on past histories. It then controls the bias arising from the non-experimental and sequential nature of this setting by balancing dynamically observable characteristics over time. We establish inferential guarantees of the proposed method even when the number of observable characteristics significantly exceeds the sample size. We study numerical properties of the estimator and illustrate the benefits of the procedure in an empirical application.